Spin orbit torque based electronic neuron

ABSTRACT

An electronic neuron device that includes a thresholding unit which utilizes current-induced spin-orbit torque (SOT). A two-step switching scheme is implemented with the device. In the first step, a charge current through heavy metal (HM) places the magnetization of a nano-magnet along the hard-axis (i.e. an unstable point for the magnet). In the second step, the device receives a current (from an electronic synapse) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the net synaptic current determines the final orientation of the magnetization. A resistive crossbar array may also be provided which functions as the synapse generating a bipolar current that is a weighted sum of the inputs of the device.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present patent application is related to and claims the prioritybenefit of U.S. Provisional Patent Application Ser. No. 62/300,852,filed Feb. 28, 2016, the contents of which is hereby incorporated byreference in its entirety into the present disclosure.

TECHNICAL FIELD

The present application relates to artificial neural networks, and morespecifically, to a spin orbit torque based electronic neuron.

BACKGROUND

Artificial neural networks (ANNs) attempt to replicate the remarkableefficiency of the biological brain for performing cognitive tasks suchas learning, pattern recognition and classification. At the heart of anyANN is an artificial neuron whose transfer function mimics that of abiological neuron. One of the most widely used models of an artificialneuron with an output (y) and a transfer function (ƒ) can be written asy=ƒ(Σ_(i) w_(i) x_(i)+b) where, x_(i) is an input to the neuron, w_(i)is its corresponding synaptic weight, and b is a constant bias term.Thus, the two main computational units of the artificial neuron areweighted summation of inputs followed by a thresholding operation.Traditionally, ANNs have been implemented in software running on aVon-Neumann type general-purpose computer. The implementation of largescale ANNs on general purpose computers requires significantcomputational capability and consumes energy that is orders of magnitudelarger than its biological counterpart. Recent developments in the fieldof neuromorphic computation attempt to bridge this gap by emulatingartificial neurons using custom analog/digital CMOS circuits. However,the emulation of artificial neurons using CMOS circuits remains highlyinefficient in terms of energy consumption and silicon area. Theinefficiency in CMOS based ANNs arises due to the significant mismatchbetween the functionality of a biological neuron and the CMOS deviceswhich are better suited for Boolean logic. Therefore, improvements areneeded in the field.

SUMMARY

The present disclosure provides an electronic neuron device thatincludes a thresholding unit which utilizes current-induced spin-orbittorque (SOT). A two-step switching scheme is implemented with thedevice. In the first step, a charge current through heavy metal (HM)places the magnetization of a nano-magnet along the hard-axis (i.e. anunstable point for the magnet). In the second step, the device receivesa current (from an electronic synapse) which moves the magnetizationfrom the unstable point to one of the two stable states. The polarity ofthe net synaptic current determines the final orientation of themagnetization. A resistive crossbar array may also be provided whichfunctions as the synapse generating a bipolar current that is a weightedsum of the inputs of the device.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following description and drawings, identical reference numeralshave been used, where possible, to designate identical features that arecommon to the drawings.

FIG. 1a is a diagram illustrating a spin-orbit torque based electronicneuron according to various aspects.

FIG. 1b is a diagram illustrating a switching scheme for the electronicof FIG. 1a according to various aspects.

FIG. 1c is a diagram illustrating orientation of a ferromagnet along ahard-axis of the electronic neuron of FIG. 1a as the switching scheme ofFIG. 1b is executed according to various aspects.

FIG. 2 is a plot showing the normalized energy landscape of a withuniaxial anisotrphy in the out-of-plane direction according to variousaspects.

FIG. 3 is a switching phase diagram showing probability of switching fora range of clock and write currents for the device of FIG. 1 accordingto various aspects.

FIG. 4 is a diagram illustrating a neural network configuration with mnumber of inputs and h number of hidden layer neurons used for digit(0-3) recognition according to various aspects.

FIG. 5 is a diagram illustrating a resistive crossbar network (RCN) forcomputation of weighted summation of electronic neuron inputs accordingto various aspects.

FIG. 6 is a schematic diagram illustrating a Read circuit for electronicneuron state according to various aspects.

FIG. 7 is a plot showing the variation of the probability of switchingof the FL with the synaptic current, corresponding to a clocking currentof 85 μA for different values of delay (TD) between the clocking andsynaptic currents according to various aspects.

FIG. 8a illustrates a random number generator implementation in a firststate using the device of FIG. 1 according to various aspects.

FIG. 8b illustrates a random number generator implementation in a secondstate using the device of FIG. 1 according to various aspects.

FIG. 9a illustrates a bit cell having a random number generator whichincorporates the device of FIG. 1 according to various aspects.

FIG. 9b illustrates a memory array incorporating a plurality of the bitcells of FIG. 9 a.

FIG. 9c illustrates a timing diagram for the device of FIG. 9 a.

FIG. 9d illustrates a simulated waveform of free layer magnetization forthe device of FIG. 9 a.

The attached drawings are for purposes of illustration and are notnecessarily to scale.

DETAILED DESCRIPTION

In the following description, some aspects will be described in termsthat would ordinarily be implemented as software programs. Those skilledin the art will readily recognize that the equivalent of such softwarecan also be constructed in hardware, firmware, or micro-code. Becausedata-manipulation algorithms and systems are well known, the presentdescription will be directed in particular to algorithms and systemsforming part of, or cooperating more directly with, systems and methodsdescribed herein. Other aspects of such algorithms and systems, andhardware or software for producing and otherwise processing the signalsinvolved therewith, not specifically shown or described herein, areselected from such systems, algorithms, components, and elements knownin the art. Given the systems and methods as described herein, softwarenot specifically shown, suggested, or described herein that is usefulfor implementation of any aspect is conventional and within the ordinaryskill in such arts.

FIG. 1a shows a thresholding unit 100 for an electronic neuron accordingto various aspects. The thresholding device 100 comprises a heavy metallayer 102 having high spin-orbit coupling, and a perpendicular magneticanisotrophy (PMA) free layer (FL) 104 in contact with the top surface ofthe heavy metal layer 102. The free layer 104 may have uniaxialanisotropy, which may be achieved based on the free layer 104 shape,interface, or bulk magneto-crystalline anisotropy. FIG. 2 illustrates asample energy landscape of the free layer 104, wherein two energy minimapoints (stable magnetization points) are separated by an anisotropybarrier. In addition, the free layer 104 is part of a magnetic tunneljunction (MTJ) 106 comprising the free layer 104, an oxide tunnelbarrier (TB) 108 and a PMA pinned layer (PL) 110. In certainembodiments, the MTJ 106 is cylindrical in shape as shown.

In the HM layer 102, when the spin Hall effect (SHE) is the dominantunderlying physical mechanism in play, a flow of charge current throughthe HM layer 102 generates pure spin current in the direction transverseto the charge current due to preferential scattering of different spinsto different directions. This pure spin current is then used to controlthe free layer 104 on top, via the spin-transfer torque effect.

In certain embodiments, the HM layer 102 may comprise beta-Tantalum,Tungsten, or Platinum. The free layer 104 and pinned layer 110 maycomprise any ferromagnetic material including, but not limited to, CoFeor CoFeB. The oxide tunnel barrier 108 may comprise an oxide materialsuch as MgO.

According to certain aspects, a two-step switching scheme is applied tothe thresholding device 100. The two-step switching scheme a) minimizesthe required current from the electronic synapse, and b) utilizes thespin hall effect for an energy efficient thresholding operation. Asshown in FIG. 1b , for the first step, a charge current (I_(Clock)) issupplied through the HM layer 102 (between terminals B and C of FIG. 1a) which generates a torque to align the free layer 104 magnetization inthe ±y direction as shown in FIG. 1a . In other words, I_(Clock) alignsthe free layer 104 magnetization along the hard-axis of the magnet, i.e.the unstable point in the energy landscape (labelled as MS in FIG. 2).Let us define this switching stage as “hard-axis switching”.Subsequently in the second step, electronic synapses (not shown)connected to the thresholding device 100 drive a charge current(I_(write), FIG. 1b ) between terminals A and C of the device 100 (asshown in FIG. 1a ). In the illustrated embodiment, temporal overlapbetween the two pulses has been neglected but the second pulse isapplied after the first pulse with negligible delay. However, it shallbe understood that the pulses may overlap in certain embodiments. Thenet synaptic current (I_(write)) flowing through the MTJ 106 exerts atorque on the magnetization which will align the magnet (free layer 104)to either one of the easy axis directions along the ±z axis (as shown inFIG. 1a ). This step is referred to as “easy-axis switching”. Thedirection of torque generated by I_(write) depends on the polarity ofthe applied net synaptic current. If the synaptic current is a positivevalue, the sign of the torque is such that the free layer 104magnetization becomes anti-parallel (AP) to the magnetization of thepinned layer 110. On the other hand, a negative synaptic current placesthe free layer 104 magnetization parallel (P) to that of the pinnedlayer 110. The P and AP states of the MTJ 106 correspond to the low andhigh (binary) outputs of the neuron made up of the MTJ 106. The proposedthresholding device therefore mimics the operation of a biologicalneuron ‘firing’ a pulse when the synaptic signal exceeds a certainthreshold.

The direction and the magnitude of spin current and its spinpolarization in the SHE can be determined from the relationship,J_(S)=θS_(H)(σ×J_(q)) where J_(S) and J_(q) are the transverse spincurrent and charge current, respectively, θ_(SH) is a material-dependentspin Hall angle, and σ is the polarization of the spin current.Magnetization dynamics of the free layer 104 are obtained by solvingLandau-Lifshitz-Gilbert equation with additional term to account for thetorque due to transverse spin current per equation (1) below:

$\begin{matrix}{\frac{d\; \hat{m}}{dt} = {{- {\gamma \left( {\hat{m} \times H_{eff}} \right)}} + {\alpha \left( {\hat{m} \times \frac{d\; \hat{m}}{dt}} \right)} + {\frac{1}{{qN}_{s}}\left( {\hat{m} \times I_{s} \times \hat{m}} \right)}}} & (1)\end{matrix}$

where {circumflex over (m)} is the unit vector of free layermagnetization, γ=2μ_(s)μ₀/h is the gyromagnetic ratio for electron[rad·m/(A·s)], H_(eff) is the effective magnetic field [A/m], andI_(S)=θ_(SH)(A_(MTj)/A_(HM))1_(q){circumflex over (σ)} is the spincurrent injected into the free-layer [A]. Ns is the number of spins inthe free layer defined as M_(S)V/μ_(s) where M_(S) is saturationmagnetization [A/m], V the volume of the free layer (m³), and μ_(B) theBohr magneton (A·m2). The effective field H_(eff) includes shapeanisotropy field H_(shape)=−(N_(XX), N_(YY), N_(ZZ))M_(S) thedemagnetization factors, N_(XX), N_(YY), N_(ZZ) for elliptical disks,magnetocrystalline anisotropy H_(Ku2) perpendicular to the free layer104 plane direction, external magnetic field H_(a), and thermalfluctuation field H_(thermal) given by

${H_{i,{thermal}}(t)} = {\sqrt{\frac{\alpha}{1 + \alpha^{2}}\frac{2k_{B}T}{\gamma \; \mu_{0}M_{S}V\; \delta_{t}}}G_{0,1}}$

where, G_(0,1) is a Gaussian distribution with zero mean and unitstandard deviation, k_(B) the Boltzmann constant, T is the temperature,δ_(t) the simulation time-step, chosen as 0.1 ps in this example.

To determine the appropriate magnitude of clock and write currents forthe device 100, a switching phase diagram for a range of clock and writecurrents is constructed as shown in FIG. 3. For each set of clock andwrite currents, ˜100,000 stochastic LLG simulations were carried out toobtain the statistics of switching. For simplicity, the rise and falltimes of the pulses were set to zero and the pulse width for clock andwrite currents were set to 2 ns and 1 ns, respectively. As it can beobserved from FIG. 3, when clock current is large enough, the amount ofwrite current needed to achieve successful switching is on the order offew μA, just enough to overcome thermal fluctuations and tilt the magnetin the desired direction. Although some amount of the synaptic currentflows through the heavy metal layer 102, the spin-orbit torque generateddue to this minimal current is expected to have negligible impact on themagnetization of the free layer 104. Thus the device 100 facilitatesfast and energy-efficient threshold operation by utilizing Spin-Halleffect for “hard-axis switching” and minimal synaptic current fordeterministic “easy-axis switching”.

According to one embodiment, an arrangement of a neural network 400 withm number of inputs 402, h number of hidden layer neurons 404, andoutputs 406 according to one embodiment is shown in FIG. 4. Consider ahardware mapping for the hidden layer of the neural network 400 as shownin FIG. 5. The hidden layer can be represented by a resistive crossbarnetwork (RCN) 502 having a plurality of resistive memory elements 508,where each row crossbar 504 provides the corresponding input to all theneurons in that layer while each column crossbar 506 provides the netsynaptic current to the spin neurons made up of MTJs 106 of thresholdingdevices 100. The row and column crossbars comprise a conductivematerial, such as copper, gold, aluminum, or the like. The resistivememory elements may comprise, but are not limited to, Phase Changememories (e.g., GeSbTe memristors), Ag—Si memristors or even spintronicdevices.

In order to implement bipolar weights, two row crossbars 504 are usedfor each input I as shown in FIG. 5 (with each input I in FIG. 5corresponding to an input 402 in FIG. 4). Each input I drives the gatesof the two pass transistors 510, one of whose switched terminals areconnected to the RCN 502 while the other switched terminal is connectedto a positive (+Vs) and negative (−Vs) supply respectively as shown. Forefficient operation of the RCN 502, the input switches should have avery low ON resistance to minimize the voltage drop across them. If theweight for a particular input is positive, then the conductancecorresponding to +Vs is programmed to the corresponding weight, whilethe conductance corresponding to −Vs is programmed to a very high OFFresistive state and vice versa. Each column 506 of the RCN 502 isconnected to the pinned layer 110 of the MTJ 106 (which forms theneuron) of the device 100, such that the device 100 receives theresultant synaptic current from the RCN 502 (with the synaptic currentpath flowing from the pinned layer 106, through the MTJ 106, and out toterminal C of FIG. 1 as discussed above). Considering the conductance inthe path of the net synaptic current while flowing through thespin-neuron (device 100) to be GG_(ss), the charge current received bythe j-th neuron is given by I_(qj)=G_(S)·Σ_(i=1)^(m)(G_(ij+)·V_(i+)+G_(ij+)·V_(i−))/(G_(s)+Σ_(i=1)^(m)+G_(ij−)))∝Σ_(i=1) ^(m)(G_(ij+)·V_(i+)+G_(ij−)·V_(i−)).

Therefore, the charge current Il_(qqqq) is proportional to the weightedsummation of the inputs (W_(ii)) and the synaptic weights (GG_(iiqq)).The sign of the charge current determines the direction of the resultantspin current and hence the final state of the nano-magnet in the device100.

According to one embodiment, interlayer communication is performed usinga read circuit as shown in FIG. 6. A reference MTJ 602 in the AP stateis utilized to form a resistive divider network which drives an inverter604. For a positive charge current input to the neuron, the MTJ 606 inthe SOT-based neuron is switched to the AP state. Thus the outputvoltage after the inverter stage 604 switches to V_(DD) which drives thegates of the two input pass transistors 510 of the RCN 502 in thesucceeding stage.

In one test example, the neural network was designed to recognize thefirst 4 digits from the MNIST machine learning dataset. The images weredownscaled to size 8×8 and 100 images were utilized for evaluating theperformance of the network. The network consisted of 25 hidden layerneurons and 4 output layer neurons. The presently disclosed neuromorphicarchitecture falls into the category of hardware that utilizes off-chiplearning. The weights and biases obtained from offline training of thenetwork using backpropagation algorithm were mapped to conductancevalues of a resistive crossbar network similar to RCN 502. The mappingwas done assuming a 5 bit discretization in the resistance levels of thecrossbar network and a dynamic range (ratio of highest to lowestresistance in the array) of 20. Input currents obtained from SPICEsimulations of the RCN 502 were then used to solve stochasticmagnetization dynamics for the SOT based neuron. For the first stage ofthe switching process, a charge current of ˜85 μA (from FIG. 3) was usedto orient the nano-magnet in the hard-axis position within a duration of2 ns, resulting in a power consumption of ˜7.22 μW per neuron. The fastand energy efficient “hard-axis switching” is mainly attributed to aspin injection efficiency of 4.71 resulting from SOT. In the next step,the net synaptic charge current from the RCN 502 drives the magnet toone of its stable magnetization states. The operating supply voltages ofthe RCN were limited by the minimum current required todeterministically switch the spin neuron in the appropriate direction(FIG. 3). For this work, the supply voltages were maintained at ±1 V andthe network accuracy was determined by running 100 stochasticsimulations of the network for each input image from the total set of100 images of the dataset. This was performed utilizing the switchingprobability curve obtained from FIG. 3 to capture the effect of themagnet's non-deterministic switching on the accuracy of the network. Theaccuracy of the network over the set of 10,000 simulation runs was ˜80%with reference to an accuracy of ˜87% without thermal fluctuations.Simulation of the read circuit for the neuron state was performed usingthe NEGF based transport simulation framework proposed in. The averagepower consumption per neuron obtained from SPICE simulations of theneural network was ˜351.94 μW.

TABLE I Simulation Parameters for SOT-based neuron Parameters Value Freelayer volume Free layer volume π/s × 40 × 40 × 1.5 nm SaturationMagnetization 1000 kA/m Spin Hall angle 0.3 Spin Hall metal dimension 40× 40 × 2 nm³ Spin Hall metal resistivity 200 μΩ-cm Gilbert DampingFactor   0.0122 Energy Barrier 31.44 KT MgO Thickness 1.0 nm Programmingrange of RCN 8-160 KΩ No. of programming levels in RCN 32   RCNoperating voltage 1 V

In order to perform an iso-throughput comparison with digital CMOStechnology, a neural network hardware was synthesized using a standardcell library in 45 nm commercial CMOS technology. A 5-bit precision wasused for the weights and each neuron was pipelined after every stage ofmultiplication and addition. The average power consumption per neuronwas ˜1.06 mW.

In order to assess the functionality of the presently disclosed devicedue to the presence of a finite delay between the I_(Clock) andI_(Write) signals, we determine the variation of the probability ofswitching P_(SW) of the free layer 104 with the synaptic current,corresponding to a clocking current of 85 μA (FIG. 7). Once themagnetization is put in its “hard-axis”, its relaxation to “easy-axis”can be described by a characteristic relaxation time constant,

${\tau_{D} = \frac{1 + \alpha^{2}}{\alpha \; \gamma \; H_{K}}},$

where H_(K) is effective anisotropy field. Using simulation parametersused above, the relaxation time constant τ_(D) is calculated as 3.5 ns.As a result, if the delay time between I_(Clock) and I_(Write) is lessthan then the functionality of the proposed neuron would not besignificantly affected. A worst case simulation of the feed-forward ANNwith an average delay of 1 ns between the clocking and synaptic currentsfor each neuron in the network show a degradation in classificationaccuracy by ˜5% only. The inherent error resiliency of such neuralcomputing algorithms helps in nullifying the effect of delay betweenclocking and synaptic currents to a large extent.

In certain embodiments, the device 100 may be implemented as part of arandom number generator as shown in FIGS. 8a and 8b . In such a case,the device 100 is operated with the switching scheme described above,except only the first step of the switching scheme is performed (i.e.,the charge current is supplied through the heavy metal layer 102 toplace the magnetization of the free layer 104 in the direction of thehard axis in the “unstable region” of position 2 in FIG. 8a ). Insteadof then applying the second “write” current, the current through theheavy metal layer is simply turned off, allowing the random thermalfield to tilt the magnetization of the free layer 104 closer to one ofthe two stable orientations as shown in FIG. 8b . This results in thedevice 100 acting as a truly random number (TRN) generator.

FIG. 9a shows a spin dice (SD) bit cell 902 which includes the thresholddevice 100 in combination with a single access transistor 904 to providea TRN generator. As shown, the control input (e.g., the gate) of thetransistor 904 is connected to a word line (WL) from a memory array andthe switching terminals of the transistor 904 (e.g., source and drain)are connected between a bit line (BL) of the memory array and the pinnedlayer of the MTJ as shown. FIG. 9b shows a further embodiment of anarray of multiple bit cells 902 where the rows sharing a Reset-Line (RL)can be driven together simultaneously. Hence, random numbers (RNs) aregenerated simultaneously in the entire array and each row can be read ata time by asserting a particular word-line (WL) and sensing the bit-line(BL). FIGS. 9c and 9d show the spin dice and the simulated waveform offree layer magnetization, respectively.

Various aspects described herein may be embodied as systems or methods.Accordingly, various aspects herein may take the form of an entirelyhardware aspect, an entirely software aspect (including firmware,resident software, micro-code, etc.), or an aspect combining softwareand hardware aspects These aspects can all generally be referred toherein as a “service,” “circuit,” “circuitry,” “module,” or “system.”

The invention is inclusive of combinations of the aspects describedherein. References to “a particular aspect” and the like refer tofeatures that are present in at least one aspect of the invention.Separate references to “an aspect” (or “embodiment”) or “particularaspects” or the like do not necessarily refer to the same aspect oraspects; however, such aspects are not mutually exclusive, unless soindicated or as are readily apparent to one of skill in the art. The useof singular or plural in referring to “method” or “methods” and the likeis not limiting. The word “or” is used in this disclosure in anon-exclusive sense, unless otherwise explicitly noted.

The invention has been described in detail with particular reference tocertain preferred aspects thereof, but it will be understood thatvariations, combinations, and modifications can be effected by a personof ordinary skill in the art within the spirit and scope of theinvention.

1. A thresholding device for an electronic neuron, comprising: a) aheavy metal layer having a high spin orbit coupling; b) a perpendicularmagnetic anisotropy free layer having a bottom surface in contact with atop surface of the heavy metal layer; c) a perpendicular magneticanisotropy pinned layer; and d) an oxide tunnel barrier connectedbetween the free layer and the pinned layer, wherein the pinned layer,the oxide tunnel barrier, and the free layer form a magnetic tunneljunction.
 2. The thresholding device of claim 1, further comprising acurrent source, the current source configured to: a) supply a firstcharge current through the heavy metal layer, from a first end of theheavy metal layer to a second end of the heavy metal layer in a firstdirection along a first axis of the heavy metal layer to generate atorque which aligns the free layer magnetization in a direction along asecond axis transverse to the first axis; and then b) supply a secondcharge current from the pinned layer, through the magnetic tunneljunction, to the second end of the heavy metal layer to exert a torqueon the magnetization of the free layer to align the free layer to eitherone of two orientations along a third axis, the third axis transverse tothe first and second axis, the two orientations anti-parallel to oneanother.
 3. The thresholding device of claim 1, wherein the secondsupply charge current is substantially zero to allow the magneticorientation of the free layer to randomly select between the twoorientations along the third axis.
 4. The thresholding device of claim1, wherein the heavy metal comprises beta-Tantalum, Tungsten, orPlatinum.
 5. The thresholding device of claim 1, wherein the pinnedlayer and the free layer comprise a ferromagnetic material.
 6. Thethresholding device of claim 5, wherein the pinned layer and the freelayer comprise CoFe or CoFeB.
 7. The thresholding device according toclaim 5, wherein the oxide tunnel barrier comprises MgO.
 8. A randomnumber generating device, comprising: a) a heavy metal layer having ahigh spin orbit coupling; b) a perpendicular magnetic anisotropy freelayer having a bottom surface in contact with a top surface of the heavymetal layer; c) a perpendicular magnetic anisotropy pinned layer; d) anoxide tunnel barrier connected between the free layer and the pinnedlayer, wherein the pinned layer, the oxide tunnel barrier, and the freelayer form a magnetic tunnel junction; and e) a current switchingdevice; and f) a current source, the current source configured to supplya first charge current through the heavy metal layer, from a first endof the heavy metal layer to a second end of the heavy metal layer in afirst direction along a first axis of the heavy metal layer to generatea torque which aligns the free layer magnetization in a direction alonga second axis transverse to the first axis, and then turn off to allow arandom thermal field to tilt the magnetization of the free layer 104closer to one of two stable orientations.
 9. An artificial neuralnetwork arrangement, comprising: a) a plurality of electricallyconductive row crossbars, each row crossbar connect to a first terminalof a plurality of resistive memory elements; b) a plurality ofelectrically conductive column crossbars, each column crossbar connectedto a second terminal of a plurality of resistive memory elements; c) aplurality of thresholding devices, each of the thresholding devicescomprising: i) a heavy metal layer having a high spin orbit coupling;ii) a perpendicular magnetic anisotropy free layer having a bottomsurface in contact with a top surface of the heavy metal layer; iii) aperpendicular magnetic anisotropy pinned layer; iv) an oxide tunnelbarrier connected between the free layer and the pinned layer, whereinthe pinned layer, the oxide tunnel barrier, and the free layer form amagnetic tunnel junction, wherein the pinned layer is connected to oneof the column crossbars.
 10. The neural network arrangement according toclaim 9, further comprising: a) a plurality of electronic switchingdevices each connecting the row crossbars to a voltage source.
 11. Theneural network arrangement according to claim 10, wherein each of theelectronic switching devices comprises a control input, and wherein eachcontrol input is connected to an input signal.
 12. The neural networkarrangement according to claim 11, wherein each input signal isconnected to the control inputs of a pair of the electronic switchingdevices; and wherein one of said pair of electronic switching devices isconnected to a positive voltage source and the other one of said pair ofelectronic switching devices is connected to a negative voltage source.13. The neural network arrangement according to claim 12, wherein theelectronic switching devices comprise a transistor.
 14. The neuralnetwork arrangement according to claim 9, wherein resistive memoryelements comprise GeSbTe memristors.
 15. The neural network arrangementaccording to claim 9, wherein the resistive memory elements compriseAg—Se memristors.